{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n    !pip install unsloth\nelse:\n    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n    import torch; v = re.match(r\"[0-9]{1,}\\.[0-9]{1,}\", str(torch.__version__)).group(0)\n    xformers = \"xformers==\" + (\"0.0.33.post1\" if v==\"2.9\" else \"0.0.32.post2\" if v==\"2.8\" else \"0.0.29.post3\")\n    !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n    !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n    !pip install --no-deps unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TGMWlrRdzwgf"
   },
   "source": [
    "### Unsloth\n",
    "\n",
    "`FastModel` supports loading nearly any model now! This includes Vision and Text models!"
   ]
  },
  {
   "cell_type": "code",
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    "outputId": "4cc24f76-8ccf-40e3-c02c-a83f4855fd0d"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2025.8.9: Fast Gemma3 patching. Transformers: 4.55.2.\n",
      "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.8.0+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.4.0\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.32.post2. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
      "Unsloth: Using float16 precision for gemma3 won't work! Using float32.\n",
      "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n"
     ]
    },
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       "model.safetensors:   0%|          | 0.00/536M [00:00<?, ?B/s]"
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    {
     "data": {
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       "chat_template.jinja: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "from unsloth import FastModel\n",
    "import torch\n",
    "max_seq_length = 2048\n",
    "fourbit_models = [\n",
    "    # 4bit dynamic quants for superior accuracy and low memory use\n",
    "    \"unsloth/gemma-3-1b-it-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gemma-3-4b-it-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gemma-3-12b-it-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gemma-3-27b-it-unsloth-bnb-4bit\",\n",
    "\n",
    "    # Other popular models!\n",
    "    \"unsloth/Llama-3.1-8B\",\n",
    "    \"unsloth/Llama-3.2-3B\",\n",
    "    \"unsloth/Llama-3.3-70B\",\n",
    "    \"unsloth/mistral-7b-instruct-v0.3\",\n",
    "    \"unsloth/Phi-4\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastModel.from_pretrained(\n",
    "    model_name = \"unsloth/gemma-3-270m-it\",\n",
    "    max_seq_length = max_seq_length, # Choose any for long context!\n",
    "    load_in_4bit = False,  # 4 bit quantization to reduce memory\n",
    "    load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory\n",
    "    full_finetuning = False, # [NEW!] We have full finetuning now!\n",
    "    # token = \"hf_...\", # use one if using gated models\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters so we only need to update a small amount of parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "fb1d2efe-35f8-4ccf-8f28-f3d63064cb4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Making `model.base_model.model.model` require gradients\n"
     ]
    }
   ],
   "source": [
    "model = FastModel.get_peft_model(\n",
    "    model,\n",
    "    r = 128, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
    "    lora_alpha = 128,\n",
    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
   },
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Data Prep\n",
    "We now use the `Gemma-3` format for conversation style finetunes. We use [Thytu's ChessInstruct](https://huggingface.co/datasets/Thytu/ChessInstruct) dataset. Gemma-3 renders multi turn conversations like below:\n",
    "\n",
    "```\n",
    "<bos><start_of_turn>user\n",
    "Hello!<end_of_turn>\n",
    "<start_of_turn>model\n",
    "Hey there!<end_of_turn>\n",
    "```\n",
    "\n",
    "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3` and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "oTXnFWbg_OIZ"
   },
   "outputs": [],
   "source": [
    "from unsloth.chat_templates import get_chat_template\n",
    "tokenizer = get_chat_template(\n",
    "    tokenizer,\n",
    "    chat_template = \"gemma3\",\n",
    ")"
   ]
  },
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      "f17d00209dde4ce793409f318423a602",
      "06961d972fad46668393474b4f643eee",
      "c3d22631abda42aeaa2f2bf75549711d",
      "0d45da3db6b84c91ad3145c24bc06502",
      "61343416b09d487fafc450824a735365",
      "60331424d3cd4a71aed6cabbdabc3b9a",
      "fa00c7889ecd45dda1e59840a72bb322",
      "5163464d76784dcb95432f8642e24fd7"
     ]
    },
    "id": "Mkq4RvEq7FQr",
    "outputId": "6f55a8d6-a015-4cfd-ceba-15cc8ddbb1df"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "155067d5c1964f81ba593e351ab43f9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe62aa2f8ce5473aae8c842bce00d36f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train.csv:   0%|          | 0.00/161M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b57fff875c2341d18895d8e5118dff27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "test.csv:   0%|          | 0.00/1.63M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8001d2ad082040c683284d594a211154",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/99000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b30129ea1434e51a3687be179f3970f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"Thytu/ChessInstruct\", split = \"train[:10000]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CBpiISFa6C"
   },
   "source": [
    "We now use `convert_to_chatml` to try converting datasets to the correct format for finetuning purposes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "4de8a935b1a7428ca199f9cd62d53de6",
      "dd63f93aa20e424693d5312d0809c778",
      "06d53bbbe9014fe088a94e332c543d0c",
      "beeafbbc918f4cdcafbfb1d67bccba8a",
      "83490c4749164e249cd01df324c1f494",
      "25553bbb43484be0999634a1e0e18476",
      "a533946196404c53a75e5662492cd104",
      "c0a648a839e34774b7add81715de9f2b",
      "b2c09a3ae1c74c9d934a4581348fd78e",
      "6dced98ee16c4377b0f8207400e79581",
      "fe3c3f4ac70b402f99a47179ded88142"
     ]
    },
    "id": "reoBXmAn7HlN",
    "outputId": "205faf31-e275-4399-aa30-aef3a85ca055"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4de8a935b1a7428ca199f9cd62d53de6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def convert_to_chatml(example):\n",
    "    return {\n",
    "        \"conversations\": [\n",
    "            {\"role\": \"system\", \"content\": example[\"task\"]},\n",
    "            {\"role\": \"user\", \"content\": example[\"input\"]},\n",
    "            {\"role\": \"assistant\", \"content\": example[\"expected_output\"]}\n",
    "        ]\n",
    "    }\n",
    "\n",
    "dataset = dataset.map(\n",
    "    convert_to_chatml\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6i5Sx9In7vHi"
   },
   "source": [
    "Let's see how row 100 looks like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dzE1OEXi7s3P",
    "outputId": "a35d2015-411e-4614-e638-4f2a689a0dad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'task': \"Given an incomplit set of chess moves and the game's final score, write the last missing chess move.\\n\\nInput Format: A comma-separated list of chess moves followed by the game score.\\nOutput Format: The missing chess move\",\n",
       " 'input': '{\"moves\": [\"c2c4\", \"g8f6\", \"b1c3\", \"c7c5\", \"g1f3\", \"e7e6\", \"e2e3\", \"d7d5\", \"d2d4\", \"b8c6\", \"c4d5\", \"e6d5\", \"f1e2\", \"c5c4\", \"c1d2\", \"f8b4\", \"a1c1\", \"e8g8\", \"b2b3\", \"b4a3\", \"c1b1\", \"c8f5\", \"b3c4\", \"f5b1\", \"d1b1\", \"d5c4\", \"e2c4\", \"a3b4\", \"e1g1\", \"a8c8\", \"f1d1\", \"d8a5\", \"c3e4\", \"f6e4\", \"b1e4\", \"b4d2\", \"f3d2\", \"c8c7\", \"d2f3\", \"c6b8\", \"c4b3\", \"b8d7\", \"e4f4\", \"c7c3\", \"e3e4\", \"a5b5\", \"e4e5\", \"a7a5\", \"f4e4\", \"a5a4\", \"b3d5\", \"h7h6\", \"d1b1\", \"b5d3\", \"e4d3\", \"c3d3\", \"e5e6\", \"d7f6\", \"e6f7\", \"g8h7\", \"d5e6\", \"g7g6\", \"h2h4\", \"f6e4\", \"b1b7\", \"h7g7\", \"b7a7\", \"d3d1\", \"g1h2\", \"e4f2\", \"a7a4\", \"d1h1\", \"h2g3\", \"f2e4\", \"g3f4\", \"e4d6\", \"f3e5\", \"h1h4\", \"f4e3\", \"d6f5\", \"e3d3\", \"f8d8\", \"e5d7\", \"h4g4\", \"f7f8b\", \"d8f8\", \"d7f8\", \"g7f8\", \"e6d5\", \"g4g3\", \"d3e4\", \"g3g2\", \"e4e5\", \"g2d2\", \"a4a8\", \"f8e7\", \"a8a7\", \"e7d8\", \"d5e6\", \"d2e2\", \"e5f6\", \"e2f2\", \"a7d7\", \"d8e8\", \"f6g6\", \"f5h4\", \"g6g7\", \"f2g2\", \"g7h7\", \"h4f3\", \"h7h6\", \"g2a2\", \"d4d5\", \"a2h2\", \"h6g7\", \"f3g5\", \"g7f6\", \"g5e4\", \"f6e5\", \"e4c3\", \"d7b7\", \"h2e2\", \"e5d4\", \"c3d5\", \"d4d5\", \"e2d2\", \"d5e5\", \"d2f2\", \"e6d5\", \"e8f8\", \"d5e6\", \"f2h2\", \"b7c7\", \"h2h5\", \"e6f5\", \"f8e8\", \"e5d6\", \"h5h6\", \"f5e6\", \"e8f8\", \"c7f7\", \"f8g8\", \"d6e5\", \"h6g6\", \"e5d5\", \"g8h8\", \"d5d6\", \"g6g5\", \"d6e7\", \"g5g7\", \"e7f6\", \"g7f7\", \"?\"], \"result\": \"1/2-1/2\"}',\n",
       " 'expected_output': '{\"missing move\": \"e6f7\"}',\n",
       " 'KIND': 'FIND_LAST_MOVE',\n",
       " 'conversations': [{'content': \"Given an incomplit set of chess moves and the game's final score, write the last missing chess move.\\n\\nInput Format: A comma-separated list of chess moves followed by the game score.\\nOutput Format: The missing chess move\",\n",
       "   'role': 'system'},\n",
       "  {'content': '{\"moves\": [\"c2c4\", \"g8f6\", \"b1c3\", \"c7c5\", \"g1f3\", \"e7e6\", \"e2e3\", \"d7d5\", \"d2d4\", \"b8c6\", \"c4d5\", \"e6d5\", \"f1e2\", \"c5c4\", \"c1d2\", \"f8b4\", \"a1c1\", \"e8g8\", \"b2b3\", \"b4a3\", \"c1b1\", \"c8f5\", \"b3c4\", \"f5b1\", \"d1b1\", \"d5c4\", \"e2c4\", \"a3b4\", \"e1g1\", \"a8c8\", \"f1d1\", \"d8a5\", \"c3e4\", \"f6e4\", \"b1e4\", \"b4d2\", \"f3d2\", \"c8c7\", \"d2f3\", \"c6b8\", \"c4b3\", \"b8d7\", \"e4f4\", \"c7c3\", \"e3e4\", \"a5b5\", \"e4e5\", \"a7a5\", \"f4e4\", \"a5a4\", \"b3d5\", \"h7h6\", \"d1b1\", \"b5d3\", \"e4d3\", \"c3d3\", \"e5e6\", \"d7f6\", \"e6f7\", \"g8h7\", \"d5e6\", \"g7g6\", \"h2h4\", \"f6e4\", \"b1b7\", \"h7g7\", \"b7a7\", \"d3d1\", \"g1h2\", \"e4f2\", \"a7a4\", \"d1h1\", \"h2g3\", \"f2e4\", \"g3f4\", \"e4d6\", \"f3e5\", \"h1h4\", \"f4e3\", \"d6f5\", \"e3d3\", \"f8d8\", \"e5d7\", \"h4g4\", \"f7f8b\", \"d8f8\", \"d7f8\", \"g7f8\", \"e6d5\", \"g4g3\", \"d3e4\", \"g3g2\", \"e4e5\", \"g2d2\", \"a4a8\", \"f8e7\", \"a8a7\", \"e7d8\", \"d5e6\", \"d2e2\", \"e5f6\", \"e2f2\", \"a7d7\", \"d8e8\", \"f6g6\", \"f5h4\", \"g6g7\", \"f2g2\", \"g7h7\", \"h4f3\", \"h7h6\", \"g2a2\", \"d4d5\", \"a2h2\", \"h6g7\", \"f3g5\", \"g7f6\", \"g5e4\", \"f6e5\", \"e4c3\", \"d7b7\", \"h2e2\", \"e5d4\", \"c3d5\", \"d4d5\", \"e2d2\", \"d5e5\", \"d2f2\", \"e6d5\", \"e8f8\", \"d5e6\", \"f2h2\", \"b7c7\", \"h2h5\", \"e6f5\", \"f8e8\", \"e5d6\", \"h5h6\", \"f5e6\", \"e8f8\", \"c7f7\", \"f8g8\", \"d6e5\", \"h6g6\", \"e5d5\", \"g8h8\", \"d5d6\", \"g6g5\", \"d6e7\", \"g5g7\", \"e7f6\", \"g7f7\", \"?\"], \"result\": \"1/2-1/2\"}',\n",
       "   'role': 'user'},\n",
       "  {'content': '{\"missing move\": \"e6f7\"}', 'role': 'assistant'}]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8Xs0LXio7rfd"
   },
   "source": [
    "We now have to apply the chat template for `Gemma3` onto the conversations, and save it to `text`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "c04cb0ba403a45b7b86a9fc14b27453c",
      "aba83f9dcc7a468f9dc3cfd9572e5fbf",
      "f51ef90e456646b1be4437da9f1b8bd4",
      "75e98179b74542c591d1da95576d5c20",
      "54342aa35ac346e08a72c8b98f208a72",
      "079d7188aa2d40b1b2b456b03207f45f",
      "b4d6bbe3e28e45f78a12d3b6894dbc8f",
      "730eaae092944b1ba124a32fd703e807",
      "63d104d065814fd6bbd89f78ef0c3344",
      "192c0442ba66453288068f79a0a3ffde",
      "407e4dfd31db4b9bbd289bc10eb7f257"
     ]
    },
    "id": "1ahE8Ys37JDJ",
    "outputId": "9f859d76-5b3d-4dc1-b403-30e6b6e9f253"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c04cb0ba403a45b7b86a9fc14b27453c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def formatting_prompts_func(examples):\n",
    "   convos = examples[\"conversations\"]\n",
    "   texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False).removeprefix('<bos>') for convo in convos]\n",
    "   return { \"text\" : texts, }\n",
    "\n",
    "dataset = dataset.map(formatting_prompts_func, batched = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ndDUB23CGAC5"
   },
   "source": [
    "Let's see how the chat template did!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "id": "AK86LIyn_OIa",
    "outputId": "5ecd561a-2334-4394-cdbf-8d456307a22b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'<start_of_turn>user\\nGiven an incomplit set of chess moves and the game\\'s final score, write the last missing chess move.\\n\\nInput Format: A comma-separated list of chess moves followed by the game score.\\nOutput Format: The missing chess move\\n\\n{\"moves\": [\"c2c4\", \"g8f6\", \"b1c3\", \"c7c5\", \"g1f3\", \"e7e6\", \"e2e3\", \"d7d5\", \"d2d4\", \"b8c6\", \"c4d5\", \"e6d5\", \"f1e2\", \"c5c4\", \"c1d2\", \"f8b4\", \"a1c1\", \"e8g8\", \"b2b3\", \"b4a3\", \"c1b1\", \"c8f5\", \"b3c4\", \"f5b1\", \"d1b1\", \"d5c4\", \"e2c4\", \"a3b4\", \"e1g1\", \"a8c8\", \"f1d1\", \"d8a5\", \"c3e4\", \"f6e4\", \"b1e4\", \"b4d2\", \"f3d2\", \"c8c7\", \"d2f3\", \"c6b8\", \"c4b3\", \"b8d7\", \"e4f4\", \"c7c3\", \"e3e4\", \"a5b5\", \"e4e5\", \"a7a5\", \"f4e4\", \"a5a4\", \"b3d5\", \"h7h6\", \"d1b1\", \"b5d3\", \"e4d3\", \"c3d3\", \"e5e6\", \"d7f6\", \"e6f7\", \"g8h7\", \"d5e6\", \"g7g6\", \"h2h4\", \"f6e4\", \"b1b7\", \"h7g7\", \"b7a7\", \"d3d1\", \"g1h2\", \"e4f2\", \"a7a4\", \"d1h1\", \"h2g3\", \"f2e4\", \"g3f4\", \"e4d6\", \"f3e5\", \"h1h4\", \"f4e3\", \"d6f5\", \"e3d3\", \"f8d8\", \"e5d7\", \"h4g4\", \"f7f8b\", \"d8f8\", \"d7f8\", \"g7f8\", \"e6d5\", \"g4g3\", \"d3e4\", \"g3g2\", \"e4e5\", \"g2d2\", \"a4a8\", \"f8e7\", \"a8a7\", \"e7d8\", \"d5e6\", \"d2e2\", \"e5f6\", \"e2f2\", \"a7d7\", \"d8e8\", \"f6g6\", \"f5h4\", \"g6g7\", \"f2g2\", \"g7h7\", \"h4f3\", \"h7h6\", \"g2a2\", \"d4d5\", \"a2h2\", \"h6g7\", \"f3g5\", \"g7f6\", \"g5e4\", \"f6e5\", \"e4c3\", \"d7b7\", \"h2e2\", \"e5d4\", \"c3d5\", \"d4d5\", \"e2d2\", \"d5e5\", \"d2f2\", \"e6d5\", \"e8f8\", \"d5e6\", \"f2h2\", \"b7c7\", \"h2h5\", \"e6f5\", \"f8e8\", \"e5d6\", \"h5h6\", \"f5e6\", \"e8f8\", \"c7f7\", \"f8g8\", \"d6e5\", \"h6g6\", \"e5d5\", \"g8h8\", \"d5d6\", \"g6g5\", \"d6e7\", \"g5g7\", \"e7f6\", \"g7f7\", \"?\"], \"result\": \"1/2-1/2\"}<end_of_turn>\\n<start_of_turn>model\\n{\"missing move\": \"e6f7\"}<end_of_turn>\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[100]['text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 100 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "74e7de6373d244d7a05b54cc2b557c01",
      "0a5d333d9edd46d2bbe5e910c9a362c1",
      "3943cd34aed7497881b65cc5487d8dcb",
      "ea5a483e248843c8a2f0dffa5cef2f9b",
      "bf34a70f7c7048d5aa9ae1f1c6f98bd5",
      "a31d15ba75bd40349f8f112500f27f5a",
      "18cd442e5d18453a96ef40976d152b5b",
      "05206e0a40044a7dadccbfed3e8353de",
      "3903b885c75d47149bc69471ba92718f",
      "dece51c7055a40a4abd1972e903b2daa",
      "59b145566f504bc09e5523310ed24638"
     ]
    },
    "id": "95_Nn-89DhsL",
    "outputId": "89fdc0da-7fc7-4bd7-8411-da8e0788b09c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Switching to float32 training since model cannot work with float16\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74e7de6373d244d7a05b54cc2b557c01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from trl import SFTTrainer, SFTConfig\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset,\n",
    "    eval_dataset = None, # Can set up evaluation!\n",
    "    args = SFTConfig(\n",
    "        dataset_text_field = \"text\",\n",
    "        per_device_train_batch_size = 4,\n",
    "        gradient_accumulation_steps = 1, # Use GA to mimic batch size!\n",
    "        warmup_steps = 5,\n",
    "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
    "        max_steps = 100,\n",
    "        learning_rate = 5e-5, # Reduce to 2e-5 for long training runs\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir=\"outputs\",\n",
    "        report_to = \"none\", # Use TrackIO/WandB etc\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C_sGp5XlG6dq"
   },
   "source": [
    "We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "00266843a26747a2a214c11ffe33bb9d",
      "08b793fa8d354511bdec7ad9cf30d462",
      "183b681c957143f0a4c50c01b2a89831",
      "ca4430b6e63e456685e0a764fb346f46",
      "7444827d8bfb4cc79dc05d1a5909949d",
      "e94421d86a43458897f4f66d91cd6989",
      "9fc1e84815614dbfa5b6724343fd9739",
      "ece49c03c22741cb8b70bcc121c176bb",
      "10e9f01ab8da4d40a442a65321b27205",
      "a01a97a60e364e8d962cc43ad1f58e48",
      "e463361f8da04184b74c46026646603d"
     ]
    },
    "id": "juQiExuBG5Bt",
    "outputId": "e0463622-8f32-4ce8-ccf3-2b01ba5ac040"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00266843a26747a2a214c11ffe33bb9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=2):   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from unsloth.chat_templates import train_on_responses_only\n",
    "trainer = train_on_responses_only(\n",
    "    trainer,\n",
    "    instruction_part = \"<start_of_turn>user\\n\",\n",
    "    response_part = \"<start_of_turn>model\\n\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dv1NBUozV78l"
   },
   "source": [
    "Let's verify masking the instruction part is done! Let's print the 100th row again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "id": "LtsMVtlkUhja",
    "outputId": "1775aef1-45d0-40e7-fa63-b901fecc1ddf"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'<bos><start_of_turn>user\\nGiven an incomplit set of chess moves and the game\\'s final score, write the last missing chess move.\\n\\nInput Format: A comma-separated list of chess moves followed by the game score.\\nOutput Format: The missing chess move\\n\\n{\"moves\": [\"c2c4\", \"g8f6\", \"b1c3\", \"c7c5\", \"g1f3\", \"e7e6\", \"e2e3\", \"d7d5\", \"d2d4\", \"b8c6\", \"c4d5\", \"e6d5\", \"f1e2\", \"c5c4\", \"c1d2\", \"f8b4\", \"a1c1\", \"e8g8\", \"b2b3\", \"b4a3\", \"c1b1\", \"c8f5\", \"b3c4\", \"f5b1\", \"d1b1\", \"d5c4\", \"e2c4\", \"a3b4\", \"e1g1\", \"a8c8\", \"f1d1\", \"d8a5\", \"c3e4\", \"f6e4\", \"b1e4\", \"b4d2\", \"f3d2\", \"c8c7\", \"d2f3\", \"c6b8\", \"c4b3\", \"b8d7\", \"e4f4\", \"c7c3\", \"e3e4\", \"a5b5\", \"e4e5\", \"a7a5\", \"f4e4\", \"a5a4\", \"b3d5\", \"h7h6\", \"d1b1\", \"b5d3\", \"e4d3\", \"c3d3\", \"e5e6\", \"d7f6\", \"e6f7\", \"g8h7\", \"d5e6\", \"g7g6\", \"h2h4\", \"f6e4\", \"b1b7\", \"h7g7\", \"b7a7\", \"d3d1\", \"g1h2\", \"e4f2\", \"a7a4\", \"d1h1\", \"h2g3\", \"f2e4\", \"g3f4\", \"e4d6\", \"f3e5\", \"h1h4\", \"f4e3\", \"d6f5\", \"e3d3\", \"f8d8\", \"e5d7\", \"h4g4\", \"f7f8b\", \"d8f8\", \"d7f8\", \"g7f8\", \"e6d5\", \"g4g3\", \"d3e4\", \"g3g2\", \"e4e5\", \"g2d2\", \"a4a8\", \"f8e7\", \"a8a7\", \"e7d8\", \"d5e6\", \"d2e2\", \"e5f6\", \"e2f2\", \"a7d7\", \"d8e8\", \"f6g6\", \"f5h4\", \"g6g7\", \"f2g2\", \"g7h7\", \"h4f3\", \"h7h6\", \"g2a2\", \"d4d5\", \"a2h2\", \"h6g7\", \"f3g5\", \"g7f6\", \"g5e4\", \"f6e5\", \"e4c3\", \"d7b7\", \"h2e2\", \"e5d4\", \"c3d5\", \"d4d5\", \"e2d2\", \"d5e5\", \"d2f2\", \"e6d5\", \"e8f8\", \"d5e6\", \"f2h2\", \"b7c7\", \"h2h5\", \"e6f5\", \"f8e8\", \"e5d6\", \"h5h6\", \"f5e6\", \"e8f8\", \"c7f7\", \"f8g8\", \"d6e5\", \"h6g6\", \"e5d5\", \"g8h8\", \"d5d6\", \"g6g5\", \"d6e7\", \"g5g7\", \"e7f6\", \"g7f7\", \"?\"], \"result\": \"1/2-1/2\"}<end_of_turn>\\n<start_of_turn>model\\n{\"missing move\": \"e6f7\"}<end_of_turn>\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Kyjy__m9KY3"
   },
   "source": [
    "Now let's print the masked out example - you should see only the answer is present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "_rD6fl8EUxnG",
    "outputId": "32e37b04-5f3c-4320-c31f-f306b5bb81b0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             {\"missing move\": \"e6f7\"}<end_of_turn>\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "44cd685c-fd0c-45a5-ecf5-e2693afa07a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = Tesla T4. Max memory = 14.741 GB.\n",
      "0.832 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CNP1Uidk9mrz"
   },
   "source": [
    "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "a2c7bd8e-ff2d-41e9-ffa1-8f8d3f3e7c20"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 10,000 | Num Epochs = 1 | Total steps = 100\n",
      "O^O/ \\_/ \\    Batch size per device = 8 | Gradient accumulation steps = 1\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (8 x 1 x 1) = 8\n",
      " \"-____-\"     Trainable parameters = 30,375,936 of 298,474,112 (10.18% trained)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [100/100 07:32, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.657700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.794700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.572900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.264200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.969500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.667800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.695000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.697700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.523400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.544300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.828000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.646300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.430800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.664700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.676600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.569600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.659800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.572700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.623300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.653000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.520700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.477100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.365900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.510300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.563000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.467100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.415400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.428400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.540300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.339300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.655600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.264400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.448800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.405300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.289400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.406200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.393700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.325900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.392900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.413800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.538700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.520600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.570800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.283400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.424000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.458500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.425600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.368800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.477200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.321900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.266400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.375900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.442000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.399700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.401100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.453300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.317500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.376300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.378200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.271100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.327700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.488800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.311700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.438700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.338900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.284300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.516800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.396900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.271800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.268000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.478100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.453400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.269900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.288000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.296100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.345400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.242600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.255000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.303600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.444200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.326300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.311000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.256900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.237600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.425400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.321500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.334900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.368400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.376800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.307200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.314200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.285300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.357800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.300500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.370500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.276700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Will smartly offload gradients to save VRAM!\n"
     ]
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCqnaKmlO1U9",
    "outputId": "889dd006-695b-4171-bdb0-f8c1ddab474a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "497.5278 seconds used for training.\n",
      "8.29 minutes used for training.\n",
      "Peak reserved memory = 4.268 GB.\n",
      "Peak reserved memory for training = 3.436 GB.\n",
      "Peak reserved memory % of max memory = 28.953 %.\n",
      "Peak reserved memory for training % of max memory = 23.309 %.\n"
     ]
    }
   ],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model via Unsloth native inference! According to the `Gemma-3` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "e0cb6539-9f2f-48da-bead-713f3c89e629"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bos><start_of_turn>user\n",
      "Given an incomplit set of chess moves and the game's final score, write the last missing chess move.\n",
      "\n",
      "Input Format: A comma-separated list of chess moves followed by the game score.\n",
      "Output Format: The missing chess move\n",
      "\n",
      "{\"moves\": [\"e2e4\", \"c7c5\", \"g1f3\", \"e7e6\", \"d2d4\", \"c5d4\", \"f3d4\", \"g8f6\", \"b1c3\", \"f8b4\", \"e4e5\", \"f6e4\", \"d1g4\", \"e4c3\", \"g4g7\", \"h8f8\", \"a2a3\", \"c3b5\", \"a3b4\", \"b5d4\", \"c1g5\", \"d8b6\", \"g5h6\", \"d4c2\", \"e1d1\", \"b6b4\", \"d1c2\", \"b8c6\", \"f1e2\", \"d7d5\", \"g7f8\", \"b4f8\", \"h6f8\", \"e8f8\", \"f2f4\", \"a7a5\", \"a1a3\", \"c8d7\", \"h1d1\", \"c6e7\", \"c2d2\", \"a5a4\", \"d1c1\", \"d7c6\", \"g2g3\", \"a8b8\", \"c1c5\", \"b7b5\", \"b2b4\", \"a4b3\", \"a3b3\", \"b8a8\", \"b3b2\", \"a8a3\", \"b2c2\", \"c6e8\", \"c5c3\", \"a3a4\", \"c3c7\", \"a4a1\", \"c7b7\", \"a1h1\", \"b7b8\", \"b5b4\", \"e2b5\", \"b4b3\", \"c2c1\", \"h1h2\", \"d2c3\", \"e7f5\", \"b5e8\", \"b3b2\", \"e8a4\", \"f8g7\", \"b8b2\", \"h2h3\", \"b2b7\", \"h3g3\", \"c3b2\", \"g3g2\", \"b2b1\", \"f5d4\", \"a4e8\", \"d4e2\", \"e8f7\", \"e2c1\", \"f7e6\", \"g7f8\", \"b1c1\", \"g2e2\", \"b7f7\", \"f8e8\", \"e6d5\", \"h7h6\", \"e5e6\", \"e8d8\", \"c1d1\", \"e2b2\", \"d5c6\", \"b2b1\", \"d1d2\", \"b1b2\", \"d2e3\", \"b2b3\", \"e3e4\", \"b3b4\", \"e4e5\", \"b4b1\", \"e6e7\", \"d8c7\", \"e7e8q\", \"c7b6\", \"e8b8\", \"b6c5\", \"b8b1\", \"c5c4\", \"b1c2\", \"c4b4\", \"f7b7\", \"b4a3\", \"?\"], \"result\": \"1-0\"}<end_of_turn>\n",
      "<start_of_turn>model\n",
      "{\"missing move\": \"a3a4\"}<end_of_turn>\n"
     ]
    }
   ],
   "source": [
    "messages = [\n",
    "    {'role': 'system','content':dataset['conversations'][10][0]['content']},\n",
    "    {\"role\" : 'user', 'content' : dataset['conversations'][10][1]['content']}\n",
    "]\n",
    "text = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    tokenize = False,\n",
    "    add_generation_prompt = True, # Must add for generation\n",
    ").removeprefix('<bos>')\n",
    "\n",
    "from transformers import TextStreamer\n",
    "_ = model.generate(\n",
    "    **tokenizer(text, return_tensors = \"pt\").to(\"cuda\"),\n",
    "    max_new_tokens = 125,\n",
    "    temperature = 1, top_p = 0.95, top_k = 64,\n",
    "    streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upcOlWe7A1vc",
    "outputId": "e400dc23-b372-4b32-89f1-3cc02dec7cad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('gemma-3/tokenizer_config.json',\n",
       " 'gemma-3/special_tokens_map.json',\n",
       " 'gemma-3/chat_template.jinja',\n",
       " 'gemma-3/tokenizer.model',\n",
       " 'gemma-3/added_tokens.json',\n",
       " 'gemma-3/tokenizer.json')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(\"gemma-3\")  # Local saving\n",
    "tokenizer.save_pretrained(\"gemma-3\")\n",
    "# model.push_to_hub(\"your_name/gemma-3\", token = \"...\") # Online saving\n",
    "# tokenizer.push_to_hub(\"your_name/gemma-3\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "MKX_XKs_BNZR"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    from unsloth import FastLanguageModel\n",
    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "        model_name = \"gemma-3\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        max_seq_length = 2048,\n",
    "        load_in_4bit = False,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "# Merge to 16bit\n",
    "if False:\n",
    "    model.save_pretrained_merged(\"gemma-3-finetune\", tokenizer, save_method = \"merged_16bit\")\n",
    "if False: # Pushing to HF Hub\n",
    "    model.push_to_hub_merged(\"hf/gemma-3-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
    "\n",
    "# Merge to 4bit\n",
    "if False:\n",
    "    model.save_pretrained_merged(\"gemma-3-finetune\", tokenizer, save_method = \"merged_4bit\",)\n",
    "if False: # Pushing to HF Hub\n",
    "    model.push_to_hub_merged(\"hf/gemma-3-finetune\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
    "\n",
    "# Just LoRA adapters\n",
    "if False:\n",
    "    model.save_pretrained(\"gemma-3-finetune\")\n",
    "    tokenizer.save_pretrained(\"gemma-3-finetune\")\n",
    "if False: # Pushing to HF Hub\n",
    "    model.push_to_hub(\"hf/gemma-3-finetune\", token = \"\")\n",
    "    tokenizer.push_to_hub(\"hf/gemma-3-finetune\", token = \"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TCv4vXHd61i7"
   },
   "source": [
    "### GGUF / llama.cpp Conversion\n",
    "To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nAlzN8HKu5Ll"
   },
   "outputs": [],
   "source": [
    "if False: # Change to True to save to GGUF\n",
    "    model.save_pretrained_gguf(\n",
    "        \"gemma-3-finetune\",\n",
    "        tokenizer,\n",
    "        quantization_method = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q974YEVPI7JS"
   },
   "source": [
    "Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S-TqCnZVu5Ll"
   },
   "outputs": [],
   "source": [
    "if False: # Change to True to upload GGUF\n",
    "    model.push_to_hub_gguf(\n",
    "        \"HF_ACCOUNT/gemma-finetune-gguf\",\n",
    "        tokenizer,\n",
    "        quantization_method = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n",
    "        token = \"hf_...\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jzmjlc3gzJVs"
   },
   "source": [
    "Now, use the `gemma-3-finetune.gguf` file or `gemma-3-finetune-Q4_K_M.gguf` file in llama.cpp.\n",
    "\n",
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "</div>\n",
    "\n  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n"
   ]
  }
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